Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

scientific processes, human interventions which are normally of low efficiency
should be avoided as much as possible, especially during workflow runtime [30].
Therefore, similar to dynamic checkpoint selection and temporal verification
strategies [20], temporal violation handling strategies should be designed to
automatically tackle a large number of temporal violations and relieve users from
heavy workload of handling those violations.
2) Cost-effectiveness. Handling temporal violations is to reduce, or ideally
remove, the delays of workflow execution by temporal violation handling strategies
with the sacrifice of additional cost which consists of both monetary cost and time
overheads. Conventional temporal violation handling strategies such as resource
recruitment and workflow restructure are usually very expensive [9, 42, 72, 82]. The
cost for recruiting new resources (e.g. the cost for service discovery and deployment,
the cost for data storage and transfer) is normally very large during workflow
runtime in distributed computing environments [72]. As for workflow restructure, it
is usually realised by the amendment of local workflow segments or temporal QoS
contracts, i.e. modifying scientific workflow specifications by human decision
makers [54]. However, due to budget (i.e. monetary cost) limits and temporal
constraints, these heavy-weight strategies (with large monetary cost and/or time
overheads) are usually too costly to be practical. To avoid these heavy-weight
strategies, recoverable violations (in comparison to severe temporal violations which
can be regarded as non-recoverable in practice) need to be identified first and then
handled by light-weight strategies (with small monetary cost and/or time overheads)
in a cost-effective fashion.
8.2 Overview of Temporal Violation Handling Strategies
As introduced in Section 6.2.1 with Figure 6.1, given the probability based temporal
consistency model, temporal violations occurred in scientific cloud workflow
systems can be classified as statistically recoverable and non-recoverable temporal
violations. Statistically recoverable temporal violations are usually resulted from
minor delays of workflow activity durations caused by the dynamic performance of
underlying cloud services. Statistically non-recoverable temporal violations are
usually brought by major delays caused by some serious technical problems such as
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